Data‐driven tuning of feedforward controller structured with infinite impulse response filter via iterative learning control

Abstract

Iterative learning control (ILC) is an effective approach for tracking control system that performs repeating tasks. However, the performance of ILC is significantly deteriorated when the reference is changed. To obtain high tracking performance for both repeating and varying tasks, a novel data-driven tuning method of feedforward controller structured with infinite impulse response (IIR) filter via ILC is developed in this study. Global optimal parameters of the feedforward controller are obtained by linear least-squares method based on the optimal feedforward control force obtained by ILC, while model information is not required. Additionally, to deal with the possible instability problem of the feedforward controller structured with IIR filter, a stable approximation approach on the basis of zero-phase-error tracking algorithm is presented. The stable approximation approach can convert the approximation problem to a convex optimisation problem. Finally, the proposed approach is compared with the standard ILC and a data-driven feedforward control structured with finite impulse response filter by two simulation studies. Simulation results demonstrate that the proposed data-driven feedforward tuning method can achieve high tracking performance and is insensitive to reference variations.